Cardiac malformations are the most common type of birth defect. Improvements in the management of complex congenital heart disease (CHD) have resulted in >90% of those born with CHD now able to survive into early adulthood. In the U.S. alone, there are more adults with CHD (~1 million individuals) than children. Many of these patients are at risk of ventricular dilatation and dysfunction especially those with a functional single ventricle. Predicting those patients who will develop maladaptive remodeling and when is difficult, but there is a wealth of potentially valuable information available in medical images, especially longitudinal MRI exams. The goal of this project is to identify new early markers of compensatory or maladaptive remodeling in CHD patients with single ventricle physiology using atlas-based MR image-derived parametric models of ventricular shape and biomechanics, and to deploy these models and data via the Cardiac Atlas Project (CAP) database. CAP is a worldwide consortium and online resource for integrating and sharing cardiac imaging examinations, together with parametric model-derived functional analyses and associated clinical information. Our multi-disciplinary team of experts in pediatric cardiology, medical imaging, computational modeling and medical informatics aims to extend this resource to patients with CHD and use the models to identify early geometric and biomechanical predictors of maladaptive remodeling and heart failure. The project will develop computational modeling tools to facilitate statistical analysis across population groups of regional heart shape and mechanical characteristics. The models and associated ontological schema will also facilitate data fusion between different imaging protocols and modalities. This project will be a collaboration with iDASH (Integrating Data for Analysis, Anonymization, and Sharing), a National Center for Biological Computing at UCSD, and will use and extend the data-sharing infrastructure developed by iDASH. The specific aims are: (1) To create a database of 60 single ventricle pathologies including some with longitudinal follow-ups. Existing CAP and iDASH infrastructure will be extended to accommodate longitudinal data from CHD patients, to enable insights into the progression of disease and the onset of failure; (2) To identify shape correlates of mechanical dysfunction in single ventricle CHD by developing atlas-based image- derived statistical models of ventricular geometry, wall motion and changes over time; (3) To use the new atlas-based models of CHD to implement computational simulations of cardiac mechanics and fluid structure interactions in these pathologies to derive potential early markers of maladaptive remodeling. This work will have a significant impact on congenital cardiology by providing non-invasive analyses of regional shape, mechanics and bloodflow. If successful, the requirement for additional invasive procedures, which have increased risk of adverse events, could be substantially reduced. A web-accessible database will also facilitate education and training in cardiology, radiology, physiology and computational biology.